Adaptive cognitive method

ABSTRACT

An adaptive cognitive method has been revealed where each distinguishable information unit, entering a given input channel, receives a unique label /identifier/ which serves as a center for the dynamic building of a structure for the presentation of knowledge on the respective information unit. A basic marker for the analysis of the correlation between the separate information units is the time quantum which is recorded—for each change—in the data base. The time quantum is the number of the shortest periods of time for the system, which have so far passed.

TECHNICAL FIELD

Artificial intellect systems, robots, expert systems, self-educatingsystems.

BACKGROUND ART

The artificial intellect systems, on the level of contemporarytechnology, include knowledge bases. A number of methods are known forthe transfer of knowledge as a system of rules, semantic networks,neuron networks, knowledge representation by frames. All of thesesystems are based on the presumption, that at least part of the contentsof their knowledge bases have been submitted and registered by people,experts in their field. It is even subtly supposed that new rules may beadded to the knowledge base singularly by man. This makes the behaviorof the expert system predictable in the case of an examined set of inputdata but it also makes the system restricted to the sphere of ruleswhich it already contains. Self-education, which is also a frequentlymet practice, does not alter the rules of the expert system set inadvance but rather fills in a bulk of statistical data which isprocessed on the basis of a method set in advance and is calculated alsoas per a set method of possible reaction. Such systems are efficientwithin restricted spheres, where for the eventualities for a finitenumber and for every possibility a preliminary expert answer may beproduced. On the other hand, the artificial intellect is characteristicwith the capacity to adapt to the environment and according to somephilosophic definitions, the intellect is the ability of the individualto adapt the environment to his own self. The proposed method offers amachine realization of basic principles via which the system can adaptitself to the environment or—upon reaching a certain level ofcomplexity—it can adapt the environment to itself.

DESCRIPTION Short Description of the Figures

FIG. 1. Plan of the system, functioning according to the method

100—Input to the system of various streams of sensor information

110—Various streams of sensor information: audio, video,temperature-related data, pressure

115—Hardware or software processors interpreting specific sensor derivedinformation and transforming that to information units with theircharacteristics.

120—Input channels for specific sensor information, transferring thesignals from the sensors.

125—Meta-control channel where the information units areregistered—targets, each of which has numerous target units for theinput channels.

130—Processor for the logical processing of the input information unitsand the generation of commands to the executive organs as well as theregistration of tasks in the meta-control channel.

135—Input channels transmitting information units representing the inputsensor information.

140—Input-Output of the processor for logical processing to meta-controlchannel

145—Command channels for the executive organs of the system.

146—Executive organs of the system.

150—Sensor information channels.

160—Information channels.

FIG. 2. Plan of the interconnection between the various informationchannels in the system.

200—Input channel, containing information units formed after the initialprocessing of the sensor information.

210—Entering into the memory of the entire information unit or recordingonly the values of the characteristics in the similar information unit,found in the memory.

220—Memory channel, containing information units from the past. In thischannel information units will be searched which conform to the units atthe input.

230—Generation of commands to the executive organs of the system so thatthe information units at the input may come as close as possible to theinformation units in the meta-command channel.

250—Objects from the real world.

FIG. 3. Information units table

FIG. 4. Table of information unit details

FIG. 5. Table of the values of the characteristics

FIG. 6. Comparison of the information units

600—Information unit 1 with characteristics H1, H2, H3

610—Information unit for the purpose of comparison

620—Information unit 2 with characteristics H1, H2, H3, H4

FIG. 7. Correlation input—target

700—Analysis of whether the generated commands approximate the targetand correction of the generation based on an alternative selection of aprevious experience. Recording in the memory of a positive/negativeexperience.

710—Input channel for details.

720—Meta command channel for details. The obligatory characteristics fora unit of target as an initial, end time quantum, maximum differencebetween the initial and end time quantum, results from the executionhave not been shown as these characteristics are auxiliary and do notexert an influence on the assessment for the proximity of oneinformation unit to another.

FIG. 8. Associations

A “Table of information units and details” has been shown as well as a“Table of values of the characteristics” and in lines 6 and 7 of the twotables the connections of the information unit{63B5E73B-30D7-4D16-93A0-35D01EABCB64} with other information units hasbeen illustrated plus the possible values of these connections in the“Table of values of the characteristics”. Line 8 of the two tables showsan exemplary association of the information unit{76E8280C-6976-4071-A108-E88848BAA1C6} with information unit{26503DB8-D6DB-43D7-9E38-022A8E5B06CF} according the criterionAssociation 1.

Let the system have an “N” number of input channels (110). Among theseinput channels there may be an audio channel, a visual channel, textdata channel, channels for other sensor-derived data such astemperature, pressure, touch. Of major importance are the channels withemergency information on the devices, building the system and they havethe highest priority in processing and decision making. Let each channelgenerate a data stream and let a specialized processor (115)/hardware orsoftware) be connected to each data stream for dividing it (110) intothe smallest, distinguishable information units for the respectivechannel. For example, for the text channel, the smallest such units arethe words, for the audio channel this is a distinguishable sound and forthe video channel—the single color spots. After the primary processing(115) a number of data, identified by the system, will be produced forthe various information units (135). The color spot has a color, size,form; the word has length, number of consonants and vowels, it also hasmeaning; sounds have pitch, frequency and tone. The system will give aunique label /identifier/ (FIG. 3 Quid) to each distinguishableinformation unit. The label /identifier/ of the information unit (FIG. 3Guid) serves as an address for attaching the derived characteristics(FIG. 4 Characteristic ID) of the information unit as well as the inputvalue of the channel which has generated it. The value of theinformation unit is its mandatory characteristic (FIG. 4 CharacteristicID=value). Part of the characteristics of the information unit are itsconnections to other information units (FIG. 5 No=100). Thecharacteristics of the information unit are used as criteria forsearching in the data base of the system (220) for finding similar,memorized information units. The similar information units obligatorilyhave a list of subsequent information units which have appeared afterthem in the input stream (135). Of all registered possible subsequentinformation units, after a given unit a list may be formed containingcharacteristics for filtering at the input stream until gain to theexpected information unit. It is possible, that a priority signal in theinput stream may break the normal process and to initiate a newcognitive chain.

In addition to the input channels (135), the system has “K” commandchannels (145). Along these channels (145), a data stream will betransferred to the executive organs (146) of the system. The smallestdistinguishable units in the data stream of the command channel are thecommands. Let the data stream in the command channel (145) be subjectedto analysis in a way similar to analyzing a data stream from an inputchannel (135). As mentioned above, each command will receive a uniquelabel and its possible parameters are attached to it as characteristicsof an information unit; a list of possible subsequent commands is alsocreated.

In its initial state, let the system have a small number of predefinedset of correspondences between the information units fed at the inputchannels (135) and a group of commands to the executive organs (146) ofthe system. In its initial stage, let the system respond to thenon-defined information units, entering the input channels (135) with arandom set of commands, dispatched along the command channels (145).

The system under description has incoming information units (135) andoutput commands (145), interpreted and brought down again to informationunits. The cognitive process must find the correlation between theinformation units, so that the generated commands (145) conform to amaximum degree to the input information units (135). The optimalconformity is defined by a meta command channel (125, 725), the datastream of which includes information units interpreted as targets. Theinformation units which are targets are normal information units (725),which possesses mandatory characteristics starting time quantum, endtime quantum, numerous information units (725), which appearance thesystem should try to enable in its input channels, end result. Thecloser the input stream of information units (135) from the observedinput channels (120) to the set of information units in the informationunit-target in the meta command channel (125, 725) the better theassessment will be for the optimality of the command stream. Theassessment of the optimality of the command stream is a dynamiccharacteristic of the information unit-target (725). The process ofgenerating commands for the command channels (145) in order to achievean input stream of certain information structures (135) passes through asearch in the data base (220) of a registration of the information unitsfrom the target set by looking for the greatest time proximity betweenthem (FIG. 3, 4, 5 time quantum). It should be borne in mind that if theinformation unit is not registered by the system (220), it cannot appearin the target set of information units (725). This permits the searchingin the base (220) for the registration of the information unit or itsprevious encounter. A sequence of commands is being searched for(145→220), which is associated with the appearance of the informationunit. If no sequence of commands is found (145→220)—that is, the targetis not routine—connections of cause and effect must be found between theinformation unit and the appearance within the time proximity (FIG. 3,4, 5 time quantum) of other information units close by. When findingmandatory information units, for the appearance of the searchedinformation unit new information units-targets will be created and sentto the meta command channel (725). To the set of anticipated informationunits of the major target, information units will be added whichrepresent the successful completion of the sub-tasks. The method is thatof trial and error. There is a possible assumption that the appearanceof a given information unit is mandatory for the appearance of anotherinformation unit which does not cause approximation to the target,although this is also a trial—the fact is being registered, that the twoinformation units appear independently of one another. This deductionmay be formed as a new information unit or as a new characteristic ofthe target information unit. All sub-tasks in the meta command channel(125, 725), all commands, generated for the command channels (145) aremarked with the identification number of the information target unitfrom which they originate. As the sub-target of a given task at a givenpoint in time is also a major task, one and the same information unit orcommand may have a number of markings, defining its associations (FIG.8), Due to the fact that the initial conditions differ when placing oneand the same task a number of times, the system generates varyingsequences of commands (145) for the achievement of one and the same task(725). All executions are associated with the set task, but they arealso associated with a given time interval as described below andanalysis can be performed on which sub-targets are present in allexecutions and also to try, during the next target identification, toreach it only by truly mandatory sub-targets. Thus the process ofachieving a given task is being optimized.

In order to be able to define the correlation between the various input(135) and command channels (145), a simple and easily distinguishablecharacteristic should set the place of a given information unit inrelation to the others. The natural characteristic via which thecorrelation between the various information units may be analyzed istime (FIG. 3, 4, 5 time quantum). As the artificial intellect system is,in principle, a dynamic system, via the time factor its various statesmay be distinguished. The smallest time period, which the system candistinguish, defines its maximal sensitivity. The changes, occurring inthe system within the said shortest time period, are not analyzed inseparate but are taken as an end result. This end result may be theaverage value of the change. In technology, this process is termed the“sampling” of a signal. Let us accept, that from the initial starting ofthe system an X quantums of time have elapsed then each change in thebase of the system at this time point is marked with the digit X. Achange in the system may be the registering of an information unit, theaddition of a new characteristic of the information unit or the additionof a new value for a given characteristic. Which information unit isbefore and which after, which information units appear simultaneouslyand even the proximity between two information units in the input streammay be found via the filtration of the size of difference between thequantum characteristics (time quantum) of the information units. Throughthe filtration of a varying difference between the time quantum of theseparate information units one may perform analysis in the differenttime intervals and to derive correlations between the separateinformation units. In this way one may search for a cause and effectconnection by filtering chains of time consecutive information unitswhich end with a given information unit. A set of simultaneouslyappearing information units may be identified as units, defining a newcompound information unit which, in its turn, has a time quantum andcharacteristics.

Another indispensable characteristic for the optimization of the systemis the usefulness of the information unit—this may be a counter for thetimes an information unit (FIG. 3 Use count) was used in the cognitiveprocess. Each comparison with the information unit (610) is a form ofuse and in each new case of comparison the counter increases by one.Each change in the information unit is also a kind of usage, making thecounter increase by one. The usefulness of a given information unit isdirectly proportional to the usage counter and inversely proportional tothe difference between the current time quantum and the maximum timequantum of the information unit. A functional dependence may be derivedbetween the two characteristics, which leads to the calculation of thepossibility of deleting the information unit from the system (220). Onthe other hand, the usage counter (FIG. 3 Use count) may be used whensorting the candidates for similar information units just like thedifference between the current time quantum and the maximum time quantumof the information unit.

The system under description has the feature of being very fast inregistering a multitude of information units (220), which will creatememory-related problems and problems with the functional efficiency.This is why a type of compression must be used. The characteristics ofthe information unit (FIG. 4, Characteristic ID) is employed as acriteria for searching the data base of the system and for findingsimilar, memorized information units (220). The information units foundmay have absolutely the same characteristics as the new informationunit; they may have more or fewer characteristics; similarcharacteristics may have contradicting values (FIG. 6). When a similarinformation unit has been found in the base (220) having the samecharacteristics, to the list of values of the respective characteristicsof the old information unit the values and the time quantum of the newinformation unit will be added (FIG. 5) and the new information unitwill be deleted. When the characteristics of the registered informationunit do not exactly conform to the characteristics of the newinformation unit, the new information unit will be registered in thedata base together with the new doubts relating to the comparison,formed as an information unit (610). The information unit (610) forcomparison includes—as characteristics—the compared information unitsand a list of discrepancies found during the comparison process. Uponthe appearance of numerous information units (610), generated fromdoubts during the comparison, the problem is solved via the formation ofa new information unit the characteristics of which are only the similar(invariant) characteristics of the examined set of information units.The invariant characteristics of the information units belonging to theexamined set will be deleted and in its place as characteristic justcreated information unit having only the invariant characteristics ofthe examined set is added. The information units for comparison,connected to the process, will be deleted while the transformedinformation units will be sent to the input channel (135) for a newanalysis.

If, during the comparison, one and the same information unit foranalysis (610) is reached many times it will be presumed that there is aconnection between the compared information units (600, 620). Theinformation unit for comparison (610) is eliminated and numerousappearances of information units with slight differences are resolved bythe summary of all information units down to one information unit, wherefor the different values of a given characteristic a list is made andeach value is marked with the time quantum when its is encountered (FIG.5).

The volume of input data (135) is so large that compression alone is nota satisfactory solution. A mechanism must be in addition for deletingthe unnecessary information units. One simple way is to delete theinformation units, the latest registered time quantum of which has morethan a set difference with the current time quantum. However, becausethere is a possibility for the existence of numerous connections of thedeleted information unit with other information units which may causeproblems with the data base (220), the deletion may relate only to theaccumulated information to the information unit (FIG. 4, 5 No. 4), bykeeping empty the information unit with the same label, characteristic(FIG. 3 Status) passive and marking of the time quantum. In this wayspace is being obtained but the structure of the connections remainsintact.

Practical Examples

A system, similar to the one described (FIG. 1), may be realized in anumber of ways, of which any specialist in this sphere is well aware of.The task may be achieved via object programming and in particular viaconnected to a data base objects, named by programmers “entities”.Without restricting the possible methods for realization, as an examplefor the easy understanding of the subject matter, a realization of thesystem is presented via a system for controlling a relational data baseand a system of rules generating queries to the data base.

As a minimum, the data base must contain the following tables:

Information Units Table (FIG. 3)

Unique identifier /a good example is the use of GUID—global uniqueidentifier/ (GUID).

Channel type (input channel, command channel, meta command channel,memory channel) (Channel type)

Channel identifier /identifier of a channel in which data stream theinformation unit has been encountered/ (Channel ID).

Status /Active, Passive/ (Status)

Usage counter /number of uses of the information unit in the cognitiveprocess/. (Use count)

Characteristics counter /Number of characteristics of the informationunit/. (Characteristics count)

Time quantum of the status change. (time quantum)

Information Units Details Table (FIG. 4)

Unique connection number. (No)

Unique identifier of the information unit (Unit ID).

Unique characteristics identifier /the characteristic is also aregistered information unit. For each information unit the mandatorycharacteristic is the value characteristic/ (Characteristic ID).

Time quantum for establishing the connection.(Time quantum)

Characteristics Value Table (FIG. 5)

Unique connection number. (No)

Value /when the information unit, used as a characteristic, does nothave a characteristic—this is its value when the information unit usedas a characteristic, on its part, has a number of characteristics thisis the unique number of the information unit/. (Value)

Time quantum of the change.(Time quantum)

Work Method

The information unit is registered in the base with a record in theInformation units table (FIG. 3), the active status (Status) is alsorecorded and the current time quantum (time quantum). The informationunit may be deleted physically if the exact correlation in the data baseis found (220). The physical deletion means the deletion of all entriesin the base, which are directly or indirectly connected to theinformation unit and the deletion of the record for the information unitin the Information units table (FIG. 3). The information unit may bedeleted logically by changing the time quantum and the status to passivein the respective record in the Information units table (FIG. 3) thevalue field may be cleared (FIG. 4, 5 No. 4) of the information unit aswell as to delete the list of values of its characteristics. Theremaining entries in the base, relating to the information unit, give bynot destroying already established connections between the informationunits a general impression, that something like with generally definedcharacteristics (FIG. 4, Characteristic ID) was existing to a timequantum . The characteristics themselves (FIG. 4, Characteristics ID)may also be deleted if the their time quantum (time quantum) issufficiently distanced in time from the current time quantum.

When identifying the information unit at the input of the Informationunits table (FIG. 4), a query is made to select records which haveunique identifier of characteristic (Characteristic ID) which can befound in the list of characteristics of the new information unit and thechannel type (Channel type) of the information unit must be the Memorychannel. The selection must be grouped as per the unique label of theinformation unit (Unit ID), to contain the selection of the use counter(Use Count), where the information unit has the unique label (GUID)equal to the unique label of the group (Unit ID), to have counter of thelines of the separate groups and to define the maximum time quantum foreach group (max(time quantum)). The selection must be sorted inascending order as per the difference between the counter for thecharacteristics of the respective information unit, recorded in itsdefinition, and the counter for the found characteristics of the sameinformation unit upon the grouping of the selection and at the sametime, the selection must be sorted in a descending order as per themaximum time quantum for a group and in descending order according tothe number of uses. Thus a list of candidates is obtained of informationunits similar to the input one, which are arranged as per the closestproximity not only by characteristics but also by the time of encounterand frequency of use. In the ordered list of candidates for similarinformation units, in the same order, the values of the separatecharacteristics of the input information unit are compared with theinformation units from the list. Upon finding a sufficiently closecorrelation in the Table of values of the characteristics (FIG. 5) newvalues of the characteristics are being added as well as a time quantumto the established similar information unit. In the opposite case, theinformation units table (FIG. 3) will receive a new record for theinformation unit in the memory channel; the table in information unitdetails (FIG. 4) entries will be made for the characteristics Firstunit, Second unit, None, Has, Different. In the table Characteristicsvalue table (FIG. 5) are recorded the unique labels for informationunits for characteristics First unit and Second unit. In the same tableand in the characteristic “None” such characteristics will be entered,which the First unit does not have but the Second has; in thecharacteristic Has such characteristics will be entered which the Firstunit has but the Second doesn't, in the characteristic “Different” thecharacteristics will be entered which have been found in both First andSecond unit but with different values, i.e., the value of thecharacteristics of First unit cannot be found in the list of values ofthe Second unit.

When a snapshot must be found, for example by the moment when a giveninformation unit has appeared, a selection is made of such informationunits from the memory channel which have at least one characteristicwith a time quantum, falling within the set interval. Such are all ofthe information units which have been active by the given point in time.

The sending of a command to the executive organ is the same as a recordof an information unit in a command channel (FIG. 3, Channeltype=Command) for the respective channel number (Channel ID).

The sending of a meta command is equivalent to a record of aninformation unit in a meta command channel (FIG. 3 Channel type=Meta).

Receiving input information from sensor channels is equivalent to arecord of an information unit in the input channel (FIG. 3, Channeltype=Input).

The information inputs from all channels (FIG. 3, Channel type),including the Memory channel (FIG. 3, Channel type=Memory) simplifiesthe unified processing as well as the examination of the correlationbetween the information units in the various channels.

Industrial Applicability

The invention may be applied as a system for the artificial intellect ofrobots, operating with different sensor channels (110) and executiveorgans (146). It may also be used as a self-educating expert system forthe analysis of large volumes of data, like the Internet environment.This presumes, that the channels described above (110), which for thesake of clarity were pictured as independent, in the case of theInternet cannot exist in separate and the input flow is a mix ofinformation which should be interpreted by various channels (115). Thisresembles the exemplary realization, where data from the variouschannels with suitable marking are registered in one and the same place.Internet pages have a clear marking pattern, indicating audio files,videos files, pictures or texts. This permits the Internet to be aninput channel (110) to the described system. Tasks in the meta channelon the other hand (125, 725) may be of the type “organize a trip for theend of the week”. The commands (145) to the executive organs (146) maybe directed at a search engine—“find sites containing “trip””—to anatural language interpreter—“interpret the text and find the date for“trip”” and if the date coincides with the end of the week, a command toa network resource for defining the rating—“give me the site's rating”,“the rating of the trip”. An example for the system may be the manualsearch performed by the user. The trial and error method could beefficient for the training of the expert system and improving theresults when the objectives are achieved.

The invention claimed is:
 1. An adaptive cognitive method, characterizedwith the following steps: transforming the sensor-derived informationfrom input streams insets information units, having an uniqueidentifier, value from the input stream caused the generation of theinformation unit and various characteristics which may be found via theprocessing of the input sensor information by specialized hardware orsoftware processors; recording tasks in a meta command channel in theform of information units with characteristics, comprising of a set ofexpected information units at the input channels; recording commands forthe executive organs of the system in a command channel in the form ofinformation units identifying commands and having as characteristics theparameters of these commands; processing the information units in theseparate channels using one and the same method and searching for acorrelation between the separate units; generating commands to theexecutive organs of the system based on the correlation between theinformation units in the meta command channel and the information unitsrecorded in the memory channel; recording in the memory channel theprocessed information units and the results of the execution of commandsto the executive organs as well as results from internal processings,represented as information units.
 2. An adaptive cognitive method,according to claim 1, which is also characterized with: searchinginformation units, similar to the newly created, in the channel memory;coping the values of the characteristics from the new unit in the foundinformation unit and deleting the new information unit if an informationunit is found in the memory channel, which has the same characteristicsas the new information units; creating an information unit forcomparison purposes if a similar information unit is found in the memorychannel, which has less, more or differing characteristics from the newinformation unit; implementing the information units for comparison forfinding the invariant characteristics of the information units from agiven set and creating an information unit having only the foundunchangeable (invariant) characteristics; recording the new informationunit in-the memory channel if it has not been deleted during theprevious step.
 3. An adaptive cognitive method, according to claim 1,which is also characterized with the search for information units,similar to the newly created, via the comparison of theircharacteristics and the creation of a new information unit as a resultfrom that comparison, which has fields identifying the first and thesecond compared information units as well as lists “has” “none”,“different” in which the results from the comparison of thecharacteristics of the examined information units will be recorded. 4.An adaptive cognitive method, according to claim 1, which is alsocharacterized with the copying of the values of the characteristics ofthe new information unit in the found information unit only when thevalue of a given characteristic is not found in the list of values ofthe respective characteristic of the found information structure.
 5. Anadaptive cognitive method, according to claim 1, which is alsocharacterized with the fact, that after the creation of an informationunit having only unchangeable (invariant) characteristics, extractedfrom a given set of similar information units, the unchangeablecharacteristics from each information unit of that set are deleted andreplaced by the created information unit which has only the unchangeablecharacteristics of the information units from the set.
 6. An adaptivecognitive method, according to claim 1, which is also characterized withthe fact, that the processing of the information units is executedaccording to their order of priority.
 7. An adaptive cognitive method,according to claim 1, which is also characterized with the fact, thateach information unit may be marked numerous times as belonging tovarious processes.
 8. An adaptive cognitive method, according to claim1, which is also characterized with the fact, that after the processingof a given information unit it is registered, if not already recorded inthe list of subsequent information units of each information unit whichhas been active during a given period prior to the current time quantum.9. An adaptive cognitive method, according to claim 1, which is alsocharacterized with the fact, that of all registered possible subsequentinformation units, after a given information unit, a list is formed withthe characteristics for filtering the input flow until fining theexpected information unit.
 10. An adaptive cognitive method, accordingto claim 1, which is also characterized with the fact, that a comparisonis being made between a set of repeatedly and simultaneously appearinginformation units defining a new compound information unit which, on itspart, has a time quantum and characteristics.
 11. An adaptive cognitivemethod, according to claim 1, which is also characterized with themarking of each change in the system by time quantum, representing thenumber of shortest distinguishable time intervals which have passed asof the initial start of the system up to the current moment.
 12. Anadaptive cognitive method, according to claim 1, which is alsocharacterized with the analysis of the correlation between theinformation units, registered in the system /which is before and whichafter and which is together with another information unit/, based on thetime quantums of the information units of the changes in the values oftheir characteristics.
 13. An adaptive cognitive method, according toclaim 1, which is also characterized with the application of a counterof the uses of a given information unit during the making of a decisionto use or delete a given information unit.
 14. An adaptive cognitivemethod, according to claim 1, which is also characterized with theinterpretation of the commands from the command channels of the systemas input information units and searching for a correlation between themand the remaining information units, registered in the system.
 15. Anadaptive cognitive method, according to claim 1, which is alsocharacterized with the achievement of a target, represented by a set ofexpected information units at the input channels via the searching andexecution of a group of commands associated with a previous achievementof the target, or searching for a connection “cause and effect” with thehelp of the time quantums of the information units which appear atcloser moments in time; finding information units necessary for theappearance of the target information unit; formulating sub-targets,which in their target set of information units contain the necessaryinformation units found during the previous step; generating commands;assessing the optimality of the generated commands according to theproximity during the comparison of the information units at the input tothe set of information units in the information unit-target in the metacommand channel.
 16. A system which includes sensors and executiveorgans with input channels for the data from the sensors, commandchannels for data (commands) to the executive organs of the system aswell as a meta command channel for the placing of targets, including amemory channel for recording data on the status of the system and whichexecutes an adaptive cognitive method according to claim
 1. 17. A systemaccording to claim 16, characterized also with the fact, that theexecutive organs permit it to connect to the Internet and to use itscontents as an input channel as well as to publish contents in theInternet or to offer services in this environment.
 18. A systemaccording to claim 16 characterized also with the fact, that theexecutive organs of the system are robot devices while the inputchannels of the system receive data from the robot's sensor channels.